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By treating slide design as an inverse planning problem, SPIRE reveals latent design intents that traditional methods miss, leading to superior personalization outcomes.
Retrieving the right prompts can boost LMM performance by up to 30%, challenging the assumption that similarity guarantees effectiveness in in-context learning.
Stop wasting compute on fine-tuning datasets with hidden capability gaps: GoalCover lets you diagnose and fix them *before* training.
Forget static rubrics: SibylSense adaptively learns rubrics at inference time, leading to more discriminative rewards and better RL performance in open-ended generation tasks.
